Overview

Dataset statistics

Number of variables9
Number of observations13543
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory952.4 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

Area is highly correlated with AspectRation and 5 other fieldsHigh correlation
AspectRation is highly correlated with Area and 4 other fieldsHigh correlation
Eccentricity is highly correlated with Area and 4 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Area and 5 other fieldsHigh correlation
ShapeFactor1 is highly correlated with AreaHigh correlation
ShapeFactor2 is highly correlated with Area and 4 other fieldsHigh correlation
ShapeFactor4 is highly correlated with Area and 5 other fieldsHigh correlation
Area is highly correlated with roundness and 2 other fieldsHigh correlation
AspectRation is highly correlated with Eccentricity and 3 other fieldsHigh correlation
Eccentricity is highly correlated with AspectRation and 3 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Area and 5 other fieldsHigh correlation
ShapeFactor1 is highly correlated with AreaHigh correlation
ShapeFactor2 is highly correlated with Area and 4 other fieldsHigh correlation
ShapeFactor4 is highly correlated with AspectRation and 4 other fieldsHigh correlation
Area is highly correlated with ShapeFactor1 and 1 other fieldsHigh correlation
AspectRation is highly correlated with Eccentricity and 2 other fieldsHigh correlation
Eccentricity is highly correlated with AspectRation and 2 other fieldsHigh correlation
roundness is highly correlated with AspectRation and 2 other fieldsHigh correlation
ShapeFactor1 is highly correlated with AreaHigh correlation
ShapeFactor2 is highly correlated with Area and 3 other fieldsHigh correlation
Area is highly correlated with AspectRation and 5 other fieldsHigh correlation
AspectRation is highly correlated with Area and 6 other fieldsHigh correlation
Eccentricity is highly correlated with Area and 6 other fieldsHigh correlation
Extent is highly correlated with AspectRation and 2 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Area and 7 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 4 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 5 other fieldsHigh correlation
ShapeFactor4 is highly correlated with Area and 5 other fieldsHigh correlation
AspectRation has unique values Unique
Eccentricity has unique values Unique

Reproduction

Analysis started2022-10-19 13:52:35.926888
Analysis finished2022-10-19 13:52:46.820074
Duration10.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Area
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12011
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.833650516 × 10-13
Minimum-3.102686723
Maximum2.577489277
Zeros0
Zeros (%)0.0%
Negative6981
Negative (%)51.5%
Memory size105.9 KiB
2022-10-19T19:22:46.911607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.102686723
5-th percentile-1.677565833
Q1-0.6661785599
median-0.03618552788
Q30.7547581474
95-th percentile1.466151753
Maximum2.577489277
Range5.680175999
Interquartile range (IQR)1.420936707

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)3.529146999 × 1012
Kurtosis-0.2200492926
Mean2.833650516 × 10-13
Median Absolute Deviation (MAD)0.7007237354
Skewness0.03117351276
Sum3.837612894 × 10-9
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:47.050279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.82764919434
 
< 0.1%
-0.68213772254
 
< 0.1%
0.38291735484
 
< 0.1%
-1.3875574664
 
< 0.1%
-1.6071375154
 
< 0.1%
-0.80975892424
 
< 0.1%
-0.47984029534
 
< 0.1%
-0.49251208964
 
< 0.1%
-0.31636805184
 
< 0.1%
-0.47029379744
 
< 0.1%
Other values (12001)13503
99.7%
ValueCountFrequency (%)
-3.1026867231
< 0.1%
-3.0912693071
< 0.1%
-3.0695956921
< 0.1%
-3.0279940581
< 0.1%
-3.0090512131
< 0.1%
-2.9700447691
< 0.1%
-2.9308272211
< 0.1%
-2.8791234121
< 0.1%
-2.8709569231
< 0.1%
-2.8592290141
< 0.1%
ValueCountFrequency (%)
2.5774892771
< 0.1%
2.5689034611
< 0.1%
2.5606089581
< 0.1%
2.5402823241
< 0.1%
2.528191261
< 0.1%
2.5209377941
< 0.1%
2.5173824271
< 0.1%
2.5089345471
< 0.1%
2.5083013331
< 0.1%
2.4951592761
< 0.1%

AspectRation
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.678901834 × 10-16
Minimum-3.004066297
Maximum2.686637435
Zeros0
Zeros (%)0.0%
Negative6908
Negative (%)51.0%
Memory size105.9 KiB
2022-10-19T19:22:47.190343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.004066297
5-th percentile-1.757496605
Q1-0.5600196125
median-0.02252528257
Q30.5911758511
95-th percentile1.802528467
Maximum2.686637435
Range5.690703731
Interquartile range (IQR)1.151195464

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-5.956494304 × 1015
Kurtosis-0.175782249
Mean-1.678901834 × 10-16
Median Absolute Deviation (MAD)0.5736913698
Skewness0.003883102117
Sum-2.50111043 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:47.325739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8230114161
 
< 0.1%
-0.15981566321
 
< 0.1%
-0.03579108731
 
< 0.1%
0.020598902961
 
< 0.1%
-0.34699005831
 
< 0.1%
-0.28856650941
 
< 0.1%
0.020464196781
 
< 0.1%
-0.21090107911
 
< 0.1%
-0.1872678451
 
< 0.1%
-0.5665043811
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-3.0040662971
< 0.1%
-2.9162795291
< 0.1%
-2.8746734921
< 0.1%
-2.8359426071
< 0.1%
-2.820954981
< 0.1%
-2.8191486761
< 0.1%
-2.7356531161
< 0.1%
-2.7355631711
< 0.1%
-2.7071965061
< 0.1%
-2.703211691
< 0.1%
ValueCountFrequency (%)
2.6866374351
< 0.1%
2.5921524711
< 0.1%
2.5887378831
< 0.1%
2.5791738941
< 0.1%
2.5343462831
< 0.1%
2.5208636011
< 0.1%
2.5024863771
< 0.1%
2.4958506731
< 0.1%
2.4897213321
< 0.1%
2.4854903531
< 0.1%

Eccentricity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.846792018 × 10-16
Minimum-2.616388585
Maximum2.416362895
Zeros0
Zeros (%)0.0%
Negative6778
Negative (%)50.0%
Memory size105.9 KiB
2022-10-19T19:22:47.464315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.616388585
5-th percentile-1.795769193
Q1-0.5712211931
median-0.0006293023985
Q30.6291123771
95-th percentile1.741917063
Maximum2.416362895
Range5.03275148
Interquartile range (IQR)1.20033357

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-5.414994822 × 1015
Kurtosis-0.3406339735
Mean-1.846792018 × 10-16
Median Absolute Deviation (MAD)0.6001461363
Skewness-0.08898359109
Sum-2.273736754 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:47.600439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8559241591
 
< 0.1%
-0.14537665871
 
< 0.1%
-0.014569650381
 
< 0.1%
0.044612295711
 
< 0.1%
-0.34407489131
 
< 0.1%
-0.28192389221
 
< 0.1%
0.044471154731
 
< 0.1%
-0.19947619611
 
< 0.1%
-0.17443435621
 
< 0.1%
-0.57813986861
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.6163885851
< 0.1%
-2.5909783671
< 0.1%
-2.5764870291
< 0.1%
-2.5616763851
< 0.1%
-2.5556157231
< 0.1%
-2.5548731411
< 0.1%
-2.5177956111
< 0.1%
-2.5177528541
< 0.1%
-2.503976921
< 0.1%
-2.501995831
< 0.1%
ValueCountFrequency (%)
2.4163628951
< 0.1%
2.3501483821
< 0.1%
2.3477291551
< 0.1%
2.3409433571
< 0.1%
2.308945431
< 0.1%
2.2992595751
< 0.1%
2.286011381
< 0.1%
2.2812145981
< 0.1%
2.2767776921
< 0.1%
2.2737115251
< 0.1%

Extent
Real number (ℝ)

HIGH CORRELATION

Distinct13535
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.97690691 × 10-15
Minimum-2.453796059
Maximum3.691861686
Zeros0
Zeros (%)0.0%
Negative6490
Negative (%)47.9%
Memory size105.9 KiB
2022-10-19T19:22:47.738127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.453796059
5-th percentile-1.690042775
Q1-0.7912415569
median0.06121181647
Q30.7631968388
95-th percentile1.545802773
Maximum3.691861686
Range6.145657745
Interquartile range (IQR)1.554438396

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-5.058593889 × 1014
Kurtosis-0.6384796447
Mean-1.97690691 × 10-15
Median Absolute Deviation (MAD)0.771730265
Skewness-0.1017161923
Sum-2.697220225 × 10-11
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:47.872164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.44394068852
 
< 0.1%
-0.68094362542
 
< 0.1%
0.58340992112
 
< 0.1%
-0.84511163832
 
< 0.1%
-0.34655758872
 
< 0.1%
-1.222178712
 
< 0.1%
0.50972488312
 
< 0.1%
-0.88908001662
 
< 0.1%
0.96813375611
 
< 0.1%
0.5596380451
 
< 0.1%
Other values (13525)13525
99.9%
ValueCountFrequency (%)
-2.4537960591
< 0.1%
-2.3969665341
< 0.1%
-2.3964485291
< 0.1%
-2.3778590731
< 0.1%
-2.3776558531
< 0.1%
-2.3670371861
< 0.1%
-2.3660933341
< 0.1%
-2.360587221
< 0.1%
-2.3568093051
< 0.1%
-2.3546812651
< 0.1%
ValueCountFrequency (%)
3.6918616861
< 0.1%
3.3379654551
< 0.1%
3.0939898691
< 0.1%
3.0693971271
< 0.1%
3.0556720571
< 0.1%
3.0043640541
< 0.1%
2.9834628431
< 0.1%
2.9148432731
< 0.1%
2.8877435391
< 0.1%
2.820730781
< 0.1%

Solidity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13522
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.070299919 × 10-16
Minimum-2.495562178
Maximum3.234558769
Zeros0
Zeros (%)0.0%
Negative6458
Negative (%)47.7%
Memory size105.9 KiB
2022-10-19T19:22:48.010681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.495562178
5-th percentile-1.769602972
Q1-0.663829137
median0.05679825725
Q30.6836657497
95-th percentile1.605103312
Maximum3.234558769
Range5.730120947
Interquartile range (IQR)1.347494887

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-9.343520478 × 1015
Kurtosis-0.3705127466
Mean-1.070299919 × 10-16
Median Absolute Deviation (MAD)0.6686068498
Skewness-0.1344654619
Sum-1.250555215 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:48.144203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49219960482
 
< 0.1%
-0.3297199112
 
< 0.1%
0.87929144792
 
< 0.1%
0.33077735232
 
< 0.1%
0.53338587132
 
< 0.1%
0.78125934862
 
< 0.1%
0.79712937592
 
< 0.1%
0.75968934662
 
< 0.1%
0.37247357232
 
< 0.1%
0.57864109242
 
< 0.1%
Other values (13512)13523
99.9%
ValueCountFrequency (%)
-2.4955621781
< 0.1%
-2.4882088781
< 0.1%
-2.4871124151
< 0.1%
-2.4843568561
< 0.1%
-2.4842259921
< 0.1%
-2.4826710761
< 0.1%
-2.480080171
< 0.1%
-2.4799139081
< 0.1%
-2.4726499151
< 0.1%
-2.4681400841
< 0.1%
ValueCountFrequency (%)
3.2345587691
< 0.1%
3.0221967731
< 0.1%
2.9402630311
< 0.1%
2.9086950641
< 0.1%
2.8198686491
< 0.1%
2.8195955791
< 0.1%
2.7977696831
< 0.1%
2.7289047761
< 0.1%
2.7117131561
< 0.1%
2.7026343641
< 0.1%

roundness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13540
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.266517477 × 10-16
Minimum-3.314464101
Maximum2.471325451
Zeros0
Zeros (%)0.0%
Negative6493
Negative (%)47.9%
Memory size105.9 KiB
2022-10-19T19:22:48.282814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.314464101
5-th percentile-1.593572317
Q1-0.7732501203
median0.05759795488
Q30.7126893867
95-th percentile1.667060183
Maximum2.471325451
Range5.785789552
Interquartile range (IQR)1.485939507

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)4.412218003 × 1015
Kurtosis-0.6397112852
Mean2.266517477 × 10-16
Median Absolute Deviation (MAD)0.7313696377
Skewness-0.06744293188
Sum3.183231456 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:48.419333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.2538032832
 
< 0.1%
-0.5730914722
 
< 0.1%
0.78167801042
 
< 0.1%
-0.38786841041
 
< 0.1%
0.30382013771
 
< 0.1%
0.23429298511
 
< 0.1%
0.62899407781
 
< 0.1%
0.64577134621
 
< 0.1%
0.29234832111
 
< 0.1%
0.33301633021
 
< 0.1%
Other values (13530)13530
99.9%
ValueCountFrequency (%)
-3.3144641011
< 0.1%
-3.1113522491
< 0.1%
-3.0526606691
< 0.1%
-3.0351698481
< 0.1%
-3.0275242241
< 0.1%
-2.957078771
< 0.1%
-2.9508230921
< 0.1%
-2.917689481
< 0.1%
-2.9008421051
< 0.1%
-2.8950027951
< 0.1%
ValueCountFrequency (%)
2.4713254511
< 0.1%
2.3965985981
< 0.1%
2.3742994531
< 0.1%
2.3668773131
< 0.1%
2.3634614451
< 0.1%
2.3482217041
< 0.1%
2.3479011171
< 0.1%
2.343801691
< 0.1%
2.3175792281
< 0.1%
2.3152160051
< 0.1%

ShapeFactor1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13521
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.435461068 × 10-12
Minimum-2.666341329
Maximum4.363482118
Zeros0
Zeros (%)0.0%
Negative6775
Negative (%)50.0%
Memory size105.9 KiB
2022-10-19T19:22:48.563391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.666341329
5-th percentile-1.520300817
Q1-0.6620506923
median-0.0003988946704
Q30.603701314
95-th percentile1.702777479
Maximum4.363482118
Range7.029823447
Interquartile range (IQR)1.265752006

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)6.966660005 × 1011
Kurtosis0.1655596619
Mean1.435461068 × 10-12
Median Absolute Deviation (MAD)0.6323832682
Skewness0.006176360544
Sum1.944181349 × 10-8
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:48.700925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.83278624582
 
< 0.1%
-0.54283980842
 
< 0.1%
0.2871434582
 
< 0.1%
-0.35199893532
 
< 0.1%
0.91199616122
 
< 0.1%
0.293944952
 
< 0.1%
0.64840471322
 
< 0.1%
-0.28152930692
 
< 0.1%
-1.3180875892
 
< 0.1%
0.24990996252
 
< 0.1%
Other values (13511)13523
99.9%
ValueCountFrequency (%)
-2.6663413291
< 0.1%
-2.6315585681
< 0.1%
-2.6295628321
< 0.1%
-2.6212707221
< 0.1%
-2.6105179091
< 0.1%
-2.6071907581
< 0.1%
-2.5917721591
< 0.1%
-2.5839206551
< 0.1%
-2.576854511
< 0.1%
-2.5719155491
< 0.1%
ValueCountFrequency (%)
4.3634821181
< 0.1%
3.6265084481
< 0.1%
3.5319391881
< 0.1%
3.4340095681
< 0.1%
3.3983758841
< 0.1%
3.3333898261
< 0.1%
3.3299779251
< 0.1%
3.3237611331
< 0.1%
3.3127829051
< 0.1%
3.2611744791
< 0.1%

ShapeFactor2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13506
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.029364403 × 10-16
Minimum-2.504045909
Maximum2.575333811
Zeros0
Zeros (%)0.0%
Negative6436
Negative (%)47.5%
Memory size105.9 KiB
2022-10-19T19:22:48.842517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.504045909
5-th percentile-1.499605646
Q1-0.9323725925
median0.08307034265
Q30.806976836
95-th percentile1.535793378
Maximum2.575333811
Range5.079379721
Interquartile range (IQR)1.739349428

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)2.481872627 × 1015
Kurtosis-1.034315979
Mean4.029364403 × 10-16
Median Absolute Deviation (MAD)0.8549935689
Skewness-0.04443499852
Sum5.00222086 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:48.979696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.022508838282
 
< 0.1%
1.1035886742
 
< 0.1%
-0.95921217942
 
< 0.1%
0.39369517622
 
< 0.1%
-1.1763788022
 
< 0.1%
0.47123725072
 
< 0.1%
0.47248709782
 
< 0.1%
-1.0347364542
 
< 0.1%
-1.4076617052
 
< 0.1%
-1.1164793482
 
< 0.1%
Other values (13496)13523
99.9%
ValueCountFrequency (%)
-2.5040459091
< 0.1%
-2.4944622661
< 0.1%
-2.4915660921
< 0.1%
-2.4759875571
< 0.1%
-2.4175418751
< 0.1%
-2.4154263881
< 0.1%
-2.4099429731
< 0.1%
-2.3789735731
< 0.1%
-2.3663171541
< 0.1%
-2.362463951
< 0.1%
ValueCountFrequency (%)
2.5753338111
< 0.1%
2.4819115881
< 0.1%
2.4720274861
< 0.1%
2.4530247221
< 0.1%
2.4128707421
< 0.1%
2.3751272461
< 0.1%
2.3379987031
< 0.1%
2.321020251
< 0.1%
2.314233811
< 0.1%
2.2933233741
< 0.1%

ShapeFactor4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13532
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.222906439
Maximum1.957891724
Zeros0
Zeros (%)0.0%
Negative6243
Negative (%)46.1%
Memory size105.9 KiB
2022-10-19T19:22:49.120325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.222906439
5-th percentile-1.769770029
Q1-0.7567146328
median0.1153378179
Q30.8124442202
95-th percentile1.453608458
Maximum1.957891724
Range4.180798163
Interquartile range (IQR)1.569158853

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)nan
Kurtosis-0.8634237718
Mean0
Median Absolute Deviation (MAD)0.7708885469
Skewness-0.2998554124
Sum4.547473509 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-10-19T19:22:49.251626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71726538322
 
< 0.1%
0.19922008032
 
< 0.1%
0.37992489622
 
< 0.1%
0.54295339982
 
< 0.1%
0.71308893252
 
< 0.1%
1.2576758732
 
< 0.1%
1.0741748072
 
< 0.1%
0.79881452632
 
< 0.1%
1.2611904812
 
< 0.1%
1.0491578182
 
< 0.1%
Other values (13522)13523
99.9%
ValueCountFrequency (%)
-2.2229064391
< 0.1%
-2.2227030161
< 0.1%
-2.2225514851
< 0.1%
-2.2218113381
< 0.1%
-2.2212247531
< 0.1%
-2.2210827011
< 0.1%
-2.2210452931
< 0.1%
-2.2210442951
< 0.1%
-2.2208661181
< 0.1%
-2.2206177771
< 0.1%
ValueCountFrequency (%)
1.9578917241
< 0.1%
1.9409084041
< 0.1%
1.9396317641
< 0.1%
1.9202840411
< 0.1%
1.9158024671
< 0.1%
1.9130421061
< 0.1%
1.9051980251
< 0.1%
1.9000012131
< 0.1%
1.8890804881
< 0.1%
1.8807781551
< 0.1%

Interactions

2022-10-19T19:22:45.445966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:36.633037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.763623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.833299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.244384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.283173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.299474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.336632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.424127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.557765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:36.785222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.883222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.947511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.356916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.395589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.412076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.455506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.541281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.672397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:36.923846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.017876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:39.063062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.471101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.510705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.528738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.574713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.657459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.782023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.049093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.129594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:39.173295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.580734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.620369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.639015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.692312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.768993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.892615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.174289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.243407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:39.285049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.694365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.732044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.756892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.810976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.883517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:46.007182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.294601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.357970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:39.835643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.807997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.843675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.874424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.929567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.997150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:46.120810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.410881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.474533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:39.941283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.920623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.958028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.991048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.049781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.109664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:46.239486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.536193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.599096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.048035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.070755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.078558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.115562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.179313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.229949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:46.349077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:37.646908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:38.712651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:40.143085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:41.175975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:42.189113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:43.227379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:44.300561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-19T19:22:45.337893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-19T19:22:49.366061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-19T19:22:49.526126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-19T19:22:49.686289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-19T19:22:50.327180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-19T19:22:46.540305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-19T19:22:46.739501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AreaAspectRationEccentricityExtentSolidityroundnessShapeFactor1ShapeFactor2ShapeFactor4
0-1.567780-1.823011-1.8559240.1580910.2477771.6336880.6650912.0263811.285548
1-1.519853-2.475280-2.3714170.682043-0.8185010.1228860.3169352.4720271.110898
2-1.431308-1.746644-1.7856930.5217520.5035791.3919810.5773041.9142071.500679
3-1.348553-2.097717-2.0937620.646282-1.9150000.4469000.3537972.100881-0.628702
4-1.331557-2.735563-2.5177530.3892881.0561662.3152160.0495122.5753341.565926
5-1.313805-1.986052-2.0001290.4571590.4850121.2994560.3570352.0324991.612125
6-1.288772-2.142390-2.1299130.121121-1.001075-0.4667130.2651692.1309941.489431
7-1.283500-2.020883-2.0298160.3270260.4314711.8569810.3174112.0386281.061521
8-1.262791-2.019214-2.0284030.3495770.1052851.5427040.2978792.0311301.428138
9-1.244375-1.800680-1.8355520.6740881.0188141.9366130.3813541.8688201.497447

Last rows

AreaAspectRationEccentricityExtentSolidityroundnessShapeFactor1ShapeFactor2ShapeFactor4
13533-0.204858-0.039233-0.0181881.6839370.5898610.1329180.1957640.1785480.437636
13534-0.2045070.1853180.2162380.4099180.9265910.2470950.3064490.0356020.752493
13535-0.2043670.0541980.079777-0.0302650.4135290.1930830.2418850.1174770.412618
13536-0.202896-0.0097880.0127460.2798070.1532610.5066330.2126910.152539-0.133032
13537-0.202896-0.579219-0.591705-0.5659681.0419390.641699-0.0616520.5343050.966648
13538-0.201007-0.0103190.012189-0.8644680.8121010.7037960.2016880.1632851.084853
13539-0.200728-0.346336-0.3433781.1518500.9930340.8173570.0412200.3811270.990352
13540-0.198073-0.363979-0.362164-0.5830560.6356840.7417470.0347970.3849130.272747
13541-0.197515-0.287869-0.281182-1.018552-0.0926130.5260950.0744280.328244-0.316679
13542-0.1966780.2665000.3000480.8237220.5376610.1479350.338261-0.0189040.968603